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The spread of SARS-CoV-2 forced many governments to implement strict measures to prevent contagion in the population, especially in 2020 and 2021, and at the beginning of 2022. Some of these measures involved social distancing, masks, hand washing, curfew, or even lockdown, among other measures (e.g., World Health Organization, 2022). Some countries, such as China, still maintain stringent measures to prevent contagion, which have a considerable effect on the daily life of their citizens. In fact, the pandemic and the changes it has brought to many people's lifestyles has had a considerable impact on the mental health of citizens (e.g., Ausín et al., 2022; Orgilés et al., 2021; Vallejo et al., 2022).
When transmission rates were high, there were no vaccines, or very few people had been vaccinated, these measures were useful for containing the transmission of COVID-19 (e.g., Haug et al., 2020). However, these measures may also be necessary if new variants of the virus appear, especially if they are more infectious, if they lead to a more severe disease, and if they are resistant to currently available vaccines. Therefore, it is possible that prevention measures may still be necessary at other moments of the pandemic or, depending on how it evolves, in some specific countries and places.
However, not everybody adhered to the restrictions imposed by governments and the recommendations of the health authorities. At some times during the pandemic, these behaviours may have contributed to increasing the transmission rates, thus subjecting the health services to even greater pressure, so it is important to know which sorts of people tend not to adhere to these restrictions and recommendations. This knowledge may be useful in new stages of the current pandemic, or for future pandemics caused by other viruses. In fact, several authors consider that further pandemics due to zoonotic viruses (transmitted from non-human animals to humans) are likely to occur in the future, because of illegal animal trafficking, deforestation, climate change, disruptive patterns of exploitation of nature, etc. (e.g., Bernstein et al., 2022; Córdoba-Aguilar et al., 2021).
According to various studies that have reported sociodemographic variables related to non-adherence to restrictions, men tend to report less compliance than women with prevention measures (e.g., Morales-Vives et al., 2022; Nivette et al., 2021). Age is also related to compliance, and younger people are more likely to display lower compliance with restrictions in different social and cultural contexts (e.g., Morales-Vives et al., 2022; Rosha et al., 2021). Finally, people with little trust in government authorities were also less likely to follow the restrictions (e.g. Nivette et al., 2021; Uddin et al., 2021).
In addition to the sociodemographic variables, several psychological variables are also related to compliance with COVID-19 restrictions. For example, various studies show the important role of personality traits in predicting compliance, Regarding the Big Five personality traits, lower levels of agreeableness and conscientiousness have been related to lower compliance (e.g., Han, 2021; Krupić et al., 2021; Nofal & Lee, 2020). More specifically, the trait Agreeableness, which is related to being altruistic, cooperative, compassionate, and kind is the strongest predictor in several studies (e.g., Blagov, 2021; Zajenkowski et al., 2020).
Other studies show that impulsivity is also related to compliance with COVID-19 restrictions (e.g., Alper et al., 2021; van Rooij et al., 2021). In fact, even before the pandemic some studies were already showing that impulsivity is related to risky behaviours and non-compliance with norms, because it involves a tendency to take speedy and irreflexive decisions, with negative consequences for the individual. For example, a systematic review by Biçaksiz & Özkan (2016) concluded that impulsivity is related to driving under the influence of alcohol and drugs, accumulating more traffic violations and exceeding the limit speed. Other studies show the relationship between impulsivity and risky behaviours that have negative consequences for health (e.g., Granö et al., 2004). Of the studies on the COVID-19 pandemic, Wismans et al. (2021) reported a negative relationship between impulsivity and compliance with social distancing and hygiene measures. Likewise, van Rooij et al. (2021) reported that impulsive people tended to show lower levels of compliance with those measures related to social distancing and staying at home. Alper et al. (2021) also found that impulsivity was negatively related to compliance, although they did not describe the restrictions included in the instrument used to evaluate compliance. Therefore, most studies seem to suggest that there is a negative relationship between impulsivity and compliance with COVID-19 restrictions. Furthermore, if Dickman's (1990) differentiation between functional and dysfunctional impulsivity is considered, it appears that is dysfunctional impulsivity that is particularly related to risk behaviors (e.g., Adan, 2012; Biçaksiz & Özkan, 2016; Ensoo et al., 2004). For this reason, this study focuses specifically on dysfunctional impulsivity, which refers to the tendency to take speedy and irreflexive decisions that have negative consequences for the individual (Morales-Vives & Vigil-Colet, 2012). In contrast, functional impulsivity refers to the tendency to take quick decisions when this strategy is optimal (Dickman, 1990).
Finally, some studies have focused on the role of the dark triad of personality in adherence to COVID-19 preventive behaviours. The concept “dark triad of personality” was originally proposed by Paulhus & Williams (2002) to describe three prominent personality profiles in the literature that are socially aversive but still within the normal range of functioning: psychopathy, Machiavellianism, and narcissism. Psychopathy involves deficits in affect (callousness, low empathy, and reduced guilt) and self-control (impulsivity, thrill-seeking) (Furnham et al., 2013; Jones & Paulhus, 2014). Machiavellianism refers to the tendency to manipulate others to achieve personal success, along with cynicism and a lack of morality (Furnham et al., 2013). Narcissism involves grandiosity, entitlement, dominance and superiority (Furnham et al., 2013). Although there is some overlap between these constructs, as they share a common core of disagreeableness, they are considered to be different and they show differential validity relations with certain external correlates (Paulhus & Williams, 2002). Regarding the relationship between the dark triad and compliance with COVID-19 restrictions, however, several studies show that individuals high in dark triad traits were less likely to engage in preventive behaviours and carry on their lives as if nothing had happened (e.g., Espinosa & Clemente, 2021; Nowak et al., 2020; Triberti et al., 2021; Zajenkowski et al., 2020). Thus, with regards to the specific correlate of compliance, according to some studies the negative traits may behave in a unified way in external validity terms. However, other studies have not found a relationship between compliance with COVID-19 restrictions and Machiavellianism (Doerfler, 2021; Modersitzki, 2021) or narcissism (Blagov, 2021; Doerfler, 2021). Therefore, it seems that the relationship between compliance and psychopathy is more consistent across studies than the relationship with narcissism or Machiavellianism.
Although many studies show the importance of personality traits in the prediction of the lack of adherence to preventive measures against COVID-19, there is a gap in the literature about the role that intellectual abilities may also play in this prediction. At the beginning of the pandemic, Xie et al. (2020) reported that working memory capacity predicted individual differences in social-distancing. They argued that working memory was key in the process of comparing multiple pieces of potentially conflicting information regarding social distancing, a comparison that had to be made to take a decision based on the merits and costs of social distancing. They found that working memory makes a unique contribution to predicting compliance with social distancing that cannot be explained by sleep quality, sociodemographic variables such as age, gender, education and income levels, and the mood-related variables anxious feeling and depressed mood. Furthermore, individual differences in Big Five personality traits and fluid intelligence do not appear to take away the unique contribution of working memory to social-distancing compliance. In fact, the Beta of fluid intelligence, which was initially significant but not very high (0.13), ceased to be significant when working memory was introduced into the model. Although this study pointed out the importance of cognitive variables, and suggested that working memory and fluid intelligence are related to social-distancing compliance, as far as we know, there are no other studies about the possible role of intelligence. Neither are there any studies that include both intelligence and personality traits related to dark triad and dysfunctional impulsivity. For this reason, the main focus of the current study is to ascertain whether intelligence can be regarded as a direct predictor of adherence to COVID-19 preventive measures, since a lower intellectual capacity may make it difficult to understand the scope of the situation, and the long-term repercussions of one's own behaviour on oneself and others. In this respect, previous studies have already shown a relationship between intelligence and health-related variables. For example, Beier and Ackerman (2003) found that mental ability explains 72% of the variance in health knowledge, far more than personality traits, and crystallized intelligence especially related to health knowledge. Likewise, Deary et al. (2010) obtained evidence on the relationship between low intelligence and mental health problems, behaviors that have a negative effect on health, and higher morbidity and mortality. More generally, here we first aim to assesses whether intelligence is related to compliance with COVID-19 preventive measures, and what its predictive role is when considered in conjunction with each of the following personality traits: narcissism, Machiavellianism, psychopathy, and dysfunctional impulsivity. Within the personality domain we are also interested in determining which of the variables referred to are the most important predictors.
The predictive aims above can be assessed via multiple regression analyses (MRA), which is expected to provide useful preliminary information. However, MRA has two main limitations here (see e.g., Lawley & Maxwell, 1973). First, MRA works well with variables that are assumed to be measured without error. Second, in MRA the relations between the predictors are viewed as “noise” and as a source of estimation problems (i.e. multicollinearity). In contrast to these assumptions, we (a) work here with unreliable variables that contain non-negligible amounts of measurement error, and (b) do not regard the relations between the predictive variables as noise but as useful and relevant information that should be modelled. For the reasons, we aim to further test a structural equation model in which both intelligence and negative personality traits are modelled as predictors of compliance. The hypothesized model is displayed in figure 1.
The model in Figure 1 shows that three intelligence scale scores (see below) are modelled as indicators of a general intelligence factor, whereas personality scores are modelled as indicators of a general factor named negative personality. This modelling addresses the issue of the measurement error above, as the predictive relations are now modelled in terms of constructs and not in terms of fallible scores. However, the substantive status of both factors is clearly different. While the intelligence factor is a well-established construct, the “negative personality” factor has a far weaker status as a latent variable. So, by no means do we claim that the dark triad and dysfunctional impulsivity behave like a higher order personality construct at a theoretical level. Rather, we contend that these negative aspects of personality share common variance, and are expected to behave as a consistent dimension in predictive terms, when the criteria to be predicted is compliance with COVID-19 measures. Note that this assumption agrees with the findings of several studies reported above. Furthermore, relationships between impulsivity and the dark personality traits narcissism (Raskin & Hall, 1981) and psychopathy (Jakobwitz & Egan, 2006) have been previously reported. Finally, a more pragmatic reason for grouping the negative personality scores in a latent factor is to “channel” the shared common variance of these scores in a single construct that is expected to be consistently related to the criterion of adherence to COVID-19 preventive measures. This reduces the overlap between specific relations among the personality measures, and thus produces a model that is expected to be more stable across different samples.
As can be seen in Figure 1, the constructs of intelligence and negative personality are allowed to be related, and the direction is that intelligence is able to predict some of these personality traits. This relation is based on the results from previous studies that obtained significant, but small effect-sized correlations between intelligence and dysfunctional impulsivity (e.g., Duran-Bonavila et al., 2017; Vigil-Colet & Morales-Vives, 2005), and that by Kowalski et al. (2018) , in which fluid intelligence significantly predicted Machiavellianism, but not narcissism or psychopathy. Other studies, however, failed to obtain significant predictive relations (O'Boyle et al. 2013).
The specification discussed above implies that intelligence can predict compliance in two ways: (a) directly, through the single path: intelligence-compliance, and (b) indirectly, through the compound path: intelligence-negative personality and negative personality-compliance (see Figure 1). In contrast, in our hypothesized model, negative personality can have only a direct effect through a single connecting path.
Method
Participants
The sample consisted of 786 participants (60.9% women) who were resident in Spain. They were between 15 and 75 years old (M = 30.19, SD = 15.22), and only 8.14% had received at least one dose of a vaccine against COVID-19. A total of 63.99% of the sample were single, 32.18% were married, 2.54% were divorced or separated, and 1.29% were widowed.
The participants were of different educational levels: 0.25% had not finished primary education, 2.93% had finished primary education, 43.13% had finished secondary education, 34.22% had finished a degree and 19.47% had postgraduate studies. As far as work is concerned, 50.76% self-identified as students, 43.13% as employees, 2.16% as unemployed, 0.77% as temporarily out of work, and 3.18% as other situations.
The household income was less than 1,000 euros in 7.12% of the sample, between 1,000 and 2,499 in 45.04% of the sample, between 2,500 and 3,999 in 31.29%, between 4,000 and 5,499 in 11.46%, and higher than 5,500 in 5.09%.
Instruments
COmpliance with pandemic COmmands Scale (COCOS; Morales-Vives et al., 2022). This questionnaire measures compliance with the COVID-19 preventive measures. It includes a wide range of measures, with items referring to social distancing, the use of face masks, hand hygiene, avoiding crowded places, not traveling without a justified reason, isolation in case of infection or having been in contact with an infected person, avoiding shared food or drinks, etc. It consists of 27 Likert-type items with 5 response options (1 = completely disagree to 5 = completely agree). The questionnaire corrects the acquiescence bias using the procedures proposed by Ferrando et al. (2009) and Lorenzo-Seva and Ferrando (2009) . Correction of acquiescence bias allows that the factor structure is the best possible and improves validity (Hernández-Dorado et al. 2021; Vigil-Colet et al., 2020). In the current sample, the marginal reliability of the content factor score estimates was ρθθ = .94.
International Cognitive Ability Resource (ICAR Sample Test; Condon & Revelle, 2014). The ICAR is a brief cognitive ability measure that assesses fluid and crystallized intelligence. It includes 16 items: four letter series items, four verbal reasoning items, four matrix reasoning items, and four three-dimensional rotation items. Letter series items consist of short letter sequences, and participants are requested to identify the next position in the sequence from among six choices. Matrix reasoning items consists of 3 × 3 arrays of geometric shapes with one of the nine shapes missing. Participants are asked to identify which of the six geometric shapes presented as response choices will best complete the stimuli. Verbal reasoning items include a variety of logic, vocabulary and general knowledge questions. The three-dimensional rotation items consist of cube renderings and participants are asked to identify which of the response choices is a possible rotation of the target stimuli. There is no time limit for solving these items. In the current study we did not include the four three-dimensional rotation items because in the pilot study we saw that they were difficult and took a long time so participants tended not to answer them. When participants reached these items, they asked to finish. For this reason, we decided to include only the letter series, verbal reasoning, and matrix reasoning items. In the current sample, the overall scores had an internal consistency reliability estimate of .78. At the series level, the corresponding reliability estimates were: .60 (Letter series), .72 (Matrix reasoning) and .61 (Verbal reasoning).
Dickman's Impulsivity Inventory (DII; Dickman, 1990). We used the Spanish adaptation developed by Chico et al. (2003) . This inventory consists of 23 binary items (true/false) which assess functional impulsivity (11 items) and dysfunctional impulsivity (12 items). We used only the 12 dysfunctional impulsivity items. Dysfunctional impulsivity refers to the tendency to take speedy and thoughtless decisions, which have negative consequences for the individual (Morales-Vives & Vigil-Colet, 2012). In the current sample we found an internal consistency of α = .77.
Dirty Dozen (DD; Jonason & Webster, 2010). We used the Spanish adaptation developed by Pineda et al. (2020) . This questionnaire has 12 Likert-type items with five response options (1 = completely disagree to 5 = completely agree). It assesses the dark triad traits of narcissism (four items), Machiavellianism (four items), and psychopathy (four items) described above. In the current sample we found an internal consistency reliability estimate of α = .83 for narcissism, α = .81 for Machiavellianism, and α = .67 for psychopathy.
Table 1 shows the descriptive statistics for each variable.
Procedure
This study was approved by the Research and Innovation Ethics Committee (CEIPSA) of Universitat Rovira i Virgili (CEIPSA-2021-PR-0002). We also obtained informed consent from all participants, in accordance with the Declaration of Helsinki.
The battery of questionnaires was administered online from February to April 2021. The exclusion criteria for participating in this study were being under 15 years old, not resident in Spain, or not providing informed consent. Participants were informed that questionnaires were anonymous and that they could drop out of the study at any time. Confidentiality and data protection were guaranteed.
As we aimed to recruit a heterogeneous sample, we disseminated the survey in several ways: a) WhatsApp groups, Facebook and Twitter; b) several Spanish associations; c) press and radio, which provided the link to the questionnaire on their websites; d) high schools. We contacted high schools from different regions in Spain by mail and/or phone, asking their teachers to tell their students aged 15 and older about the survey. We did not ask for the informed consent of the parents of participants aged 15-18 years old because the pandemic prevented us from going in person to the schools to do so and some of the adolescents accessed the survey through the social networks. However, according to Spanish legislation the personal data of minors can be processed with their consent when they are older than 14 (article 7 of Organic Law 3/2018, of 5 December, on the Protection of Personal Data and Guarantee of Digital Rights). Furthermore, once the participants had finished the questionnaire, the website allowed them to share it with other people on the social networks (e.g., WhatsApp and Facebook), which facilitated wider dissemination.
Data Analysis
It should first be taken into account that there was no missing data in the study, because the survey did not allow responders to move on to one item without answering the previous one.
In the first part of the study we carried out correlation analysis and MRA to determine whether, in addition to personality traits (dysfunctional impulsivity and the dark triad), intelligence was also a predictor of compliance with COVID-19 measures. As standardized coefficients (beta weights) are context dependent and can become very unstable in the presence of substantially correlated predictors (Johnson, 2000), we used Johnson's structural coefficients and relative weights (Johnson, 2000) to assess their relative importance. Johnson's relative weights estimate the relative contribution each variable makes to the prediction of a dependent variable, taking into account both its individual contribution and its contribution when combined with other variables. These are presented as percentages (i.e., they are divided by R2 and multiplied by 100).
The regression results from the first part of the study were used to fit the structural model discussed above in the second part of the study. Because the units of analysis were scale or sub-scale scores, measures were treated as continuous, and the model was estimated using a robust weighted least squares (WLS) criterion (see Ferrando, 2021). Model-data fit was assessed using three groups of indices that addressed different facets of fit: absolute fit (RMSR), relative fit per degree of freedom (RMSEA), and comparative fit with respect to the null independence model (CFI).
In addition to the goodness of model-data fit, the assessment of model appropriateness was extended in two directions. First, the stability of the model results was assessed in two ways: (a) using a cross-validation schema in which the total sample was randomly split in two halves, and (b) using intensive Bootstrap resampling and assessing the variability in the parameter estimates across resampling. Second, the power of the test statistic for detecting model misspecifications was assessed with the approach proposed by Lee et al. (2012) .
Statistical analyses were carried out using SPSS 28, MIMR-Raw.sps (Lorenzo-Seva et al., 2010) and Mplus v8.8 (Muthén & Muthén, 2017).
Results
Correlations between Compliance with COVID-19 Measures and the Other Variables
Table 2 shows the first-order correlations between the different variables. As can be seen, COCOS scores are correlated with all the variables, with medium effect sizes for psychopathy, Machiavellianism and dysfunctional impulsivity, and small effect sizes for intelligence and narcissism. In fact, the correlation was smallest between intelligence and compliance. In contrast, the variable most correlated with compliance was psychopathy.
Prediction of Compliance with COVID-19 Measures: Multiple Regression Analysis
In the MRA all the variables were entered into the regression equation as potential predictors of compliance with COVID-19 measures. Multiple R was .55, F(5, 780) = 53.44, p < .001, 95% CI [.49, .62]. Table 3 shows standardized regression coefficients (Beta), structure coefficients, Johnson's relative weights and bootstrap confidence intervals. The variables are ordered in terms of their contribution to the prediction of compliance, as Johnson's relative weights indicate. As can be seen, all predictors (psychopathy, Machiavellianism, narcissism, intelligence and dysfunctional impulsivity) have significant structure coefficients, with bootstrap 95% confidence intervals that did not include the zero value. Furthermore, Johnson's relative weights indicate that these five variables contribute to predicting compliance. In fact, the relative importance analysis suggests that psychopathy and dysfunctional impulsivity are the main predictors of compliance, while narcissism and especially intelligence make the lowest contribution. Although the Beta coefficients of Machiavellianism, narcissism and intelligence were not significant, the fact that the structure coefficients and Johnson's relative weights were significant suggests that these traits should also be included in the model because they contribute significantly to the prediction of compliance.
Table 3. Results of the Multiple Regression Analysis
| Scales | Beta | SC | Bootstrap 95% C.I. for SC | RW | Bootstrap 95% C.I. for RW | ||
|---|---|---|---|---|---|---|---|
| Lower | Upper | Lower | Upper | ||||
| Psychopathy | -.33** | - .92 | -.97 | -.81 | 38.9 | 25.6 | 50.3 |
| Dysfunctional Impulsivity | -.21** | -.82 | -.91 | -.70 | 29.3 | 18.0 | 43.3 |
| Machiavellianism | -.02 | -.76 | -.85 | -.62 | 17.4 | 10.7 | 26.1 |
| Narcissism | -.09 | -.55 | -.66 | -.42 | 9.9 | 4.8 | 15.7 |
| Intelligence | .03 | .35 | .20 | .47 | 4.4 | 1.4 | 10.2 |
Note. The variables are ordered according to their relative weights. SC: Structure coefficient.
RW: Relative weight (reported as percentages).
** p<.01 * p<.05
SEM: A Model of Relationships between the Different Variables and Compliance with COVID-19 Measures
The model tested in Figure 1 showed remarkable stability, both under simple cross-validation and under Bootstrap resampling (details can be obtained from the authors). In all cases, however, results systematically suggested the need to free the residual covariance between narcissism and Machiavellianism. In pure modelling terms, this means that these two measures share common variance beyond their belonging to the common hypothesized negative personality factor. In our opinion, this “doublet” is due to specific characteristics of the Spanish adaptation of the questionnaire used in this study. Indeed, inspection of the item stems suggests that both scales share specific item content that can be summarized as “taking advantage of others”. As these subscales are very short, only four items each, this overlapping is more noticeable than in longer questionnaires. Overall, and also taking into account that (a) this result has been replicated in different subsamples, (b) consistently obtained through simple cross-validation and intensive Bootstrap resampling, and (c) the estimated value of the doublet is substantial, we believe that freeing this doublet is not a trivial decision with the only aim of improving the fit in a particular sample. Rather, it is a substantively justified decision that is expected to be consistently obtained across different samples and eventual replications of the study.
With this one post-hoc modification, the proposed model fitted the data quite well. The results were: root mean square residual (RMSR) standardized = .031; root-mean-square error of approximation (RMSEA) = .046 and 90% CI [.031; .063]; and comparative fit index (CFI) = .98. By all standards this can be judged as a very acceptable fit as CFI values higher than .90, and RMSEA and RMSR values lower than .07 and .08, respectively, are indicative of an acceptable fit (Bentler, 1990; Hu et al., 1999; Steiger, 2007). The power to detect a misspecification of RMSEA = .08 was estimated at Beta = 0.99. So, the model fits well and it can also be considered to be powerful enough to detect even a small to moderate misspecification.
As a final check, the model was extended to the multiple-group case with gender as a grouping variable (see Ferrando, 2021). The strong invariance model was found to fit the data quite well, and, in relative terms, better than the weakest forms of invariance. So, it can be assumed that the model in Figure 1 functions with the same structure and properties in men and women.
The estimated standardized coefficients for the model in the total sample are in Figure 2. As a summary: (a) both factors are strongly defined by their indicators; (b) the negative personality factor has a strong and negative relation with compliance; (c) the direct predictive power of intelligence on compliance is non-significant; but (d) the indirect, compound effect is. Using the standard rules of path analysis (Wright, 1934), the overall indirect effect estimate is .20 (the result of multiplying -.33 and -.60), positive and statistically significant.
Discussion
Various studies have shown the importance of different personality traits in the prediction of compliance with measures to prevent COVID-19 infection (e.g., Blagov, 2021; Zajenkowski et al., 2020). However, little is known about the possible role of intelligence in the tendency to comply with these measures, especially considering that a higher level of intelligence could facilitate a better understanding of their implications and their usefulness. For this reason, the main goal of the current study was to determine whether intelligence is related to compliance with COVID-19 preventive measures, and what its predictive role is when considered together with the following personality traits: narcissism, Machiavellianism, psychopathy, and dysfunctional impulsivity. The literature already shows that these personality traits are related to compliance with COVID-19 preventive measures, especially psychopathy and impulsivity, which is consistent with the present results. More specifically, the results of the MRA suggest that psychopathy is the variable that makes the highest contribution to compliance, followed by dysfunctional impulsivity. Studies such as those by Espinosa & Clemente (2021) , Nowak et al. (2020) or Triberti et al. (2021) also show that psychopathy is a predictor of compliance with COVID-19 preventive measures. Likewise, several studies carried out before the pandemic showed that dysfunctional impulsivity is related to non-compliance with norms and risky behaviours (e.g., Biçaksiz & Özkan, 2016), so it is not surprising that similar results have been found for COVID-19 compliance (e.g., Alper et al., 2021, van Rooij et al., 2021), as in the current study. Previous studies, however, have provided contradictory results about the relationship between the other two dark triad traits, narcissism and Machiavellianism, and compliance with COVID-19 measures. While some studies reported that they are important predictors (e.g., Espinosa & Clemente, 2021; Nowak et al., 2020; Triberti et al., 2021; Zajenkowski et al., 2020), others did not find this relationship (e.g., Blagov, 2021; Doerfler, 2021; Modersitzki, 2021). Our present results obtained a significant first-order correlation between these traits and compliance, although MRA suggests that they are not as important predictors as psychopathy and dysfunctional impulsivity.
The MRA suggests that, contrary to our expectations, intelligence is the variable that contributes the least to the prediction of compliance. The previous study by Xie et al. (2020) showed that fluid intelligence was a significant but not highly relevant predictor of compliance with social distancing. In addition, their study did not include the personality variables assessed in the present investigation, which may explain the different results observed. In fact, the present results suggest that intelligence has only an indirect relationship with compliance, through its relationship with the negative personality traits dysfunctional impulsivity and the dark triad. Therefore, individuals with lower levels of empathy and reduced guilt, higher impulsivity and narcissistic characteristics, and a tendency to manipulate others are less likely to engage in preventive behaviours, possibly because they are more self-centered and do not care much about the wellbeing of others. Intelligence, on the other hand, might modulate the relationship between the negative personality traits and compliance. If so, more intelligent people with negative personality traits would tend not to have such low levels of compliance, probably because their higher level of intelligence allows them to better understand the situation and the consequences of contagion, at least for themselves.
The study by Xie et al. (2020) focuses on compliance with social distancing measures, so it does not assess compliance with preventive measures such as using face masks, hand hygiene, isolation in case of infection, etc. In contrast in the current study we have assessed compliance more comprehensively with a wide range of different measures. Likewise, many studies on the relationship between personality and COVID-19 compliance only assessed social distance and hygiene measures. Therefore, the present study provides a more comprehensive understanding of compliance with different COVID-19 preventive measures. However, more research is needed to understand the role intelligence and other cognitive variables play in compliance with COVID-19 measures, assessing compliance in a comprehensive way, and comparing intelligence with other predictors, such as the Big Five personality traits or sociodemographic variables.
The results of this study could be helpful to design more effective communication policies. In fact, individual differences are important to how people react emotionally to specific advertising messages (e.g., Orth et al., 2010), and, for this reason, some companies send out different messages about the same product or service to prompt an emotional response in different kinds of people. In the case of people with dark personality traits, government messages focused on pro-sociality and community interests may not be effective or may generate little emotional response, which would probably not help to improve their prevention behaviours. However, messages more focused on individuality, with an emphasis on individual gain, may increase their adherence to restrictions. Furthermore, although this study focuses on the COVID-19 pandemic, its results can be applied to programs designed to prevent the spread of other diseases, for example sexually transmitted diseases (STDs). In fact, many STDs have no visible symptoms, which makes it easier to continue spreading the disease after being diagnosed.
Some of the limitations of our study are that we did not assess science scepticism, lack of trust in experts, or belief in conspiracy theories, even though they are important variables in this field. Further studies are also needed to understand the role of intelligence together with the dark triad regarding these variables and compliance with COVID-19 preventive measures.
To sum up, the present study clearly shows the role that intelligence, impulsivity and the dark triad play in predicting compliance with COVID-19 preventive measures. However, since we have used a brief questionnaire to assess the dark triad, it would be interesting to know whether stronger results could be obtained with longer and more complete measures of these constructs. Future studies may provide a deeper understanding of this relationship, and give greater insight into the facets that play the most important roles in predicting adherence to preventive measures.
